CloudAcademy

Analyzing CPU vs GPU Performance for AWS Machine Learning

The hands-on lab is part of this learning path

Introduction to Machine Learning on AWS

course-steps 4 certification 1 lab-steps 3

Lab Steps

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Logging in to the Amazon Web Services Console
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Connecting to the Virtual Machine using SSH
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Starting a Jupyter Notebook Server
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Forwarding a Virtual Machine Port through an SSH Tunnel
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Setting Up the CPU vs. GPU Experiment
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Running the CPU vs. GPU Experiment
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Analyzing the CPU vs. GPU Experiment Results

Ready for the real environment experience?

DifficultyBeginner
Duration45m
Students80

Description

Lab Overview

Graphics processing units (GPUs) and other hardware accelerators can dramatically reduce the time taken to train complex machine learning models. In this lab, you will take control of a p2.xlarge instance equipped with an NVIDIA Tesla K80 GPU to perform a CPU vs GPU performance analysis for Amazon Machine Learning. The instance is based on the AWS deep learning AMI that comes with many machine learning libraries pre-installed. You will create a Jupyter Notebook to write code and visualize results in a single document. The TensorFlow library is used for the CPU and GPU benchmark code.

Lab Objectives

Upon completion of this Lab you will be able to:

  • Run Jupyter Notebook server and create Jupyter Notebooks for machine learning experiments
  • Configure an SSH tunnel to forward instance ports through an encrypted channel
  • Understand when GPUs can be advantageous in machine learning, and to what extent

Lab Prerequisites

You should be familiar with:

  • Working with Linux on the command-line
  • Knowledge of the Python programming language is beneficial, but not required

Lab Environment

Before completing the Lab instructions, the environment will look as follows:

After completing the Lab instructions, the environment should look similar to:

About the Author

Students6161
Labs57
Courses3
Learning paths2

Logan has been involved in software development and research for over eleven years, including six years in the cloud. He is an AWS Certified DevOps Engineer - Professional, MCSE: Cloud Platform and Infrastructure, and Certified Kubernetes Administrator (CKA). He earned his Ph.D. studying design automation and enjoys all things tech.